An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture

Abstract

Blind component separation aims to decompose a single-channel vibration signal mixture into periodic components and random transient components. In addition to periodic components, random transient components with a high degree of impulsiveness are signals of interest in practice. An adaptive signal processing method called empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into the sum of simple components termed intrinsic mode functions (IMFs). Ensemble empirical mode decomposition (EEMD) is an improvement of EMD and aims to relieve a mode mixing problem that exists in EMD. However, there is no universal standard formula that can be used to select appropriate parameters of EEMD. Improper parameters of EEMD still cause a mode mixing problem that makes a signal of a similar scale reside in some successive IMFs. An enhanced EEMD for the purpose of blind component separation is developed in this paper to respectively extract periodic components and random transient components from a single-channel vibration signal mixture. A revised spectral coherence is proposed to measure the spectral dependence between two successive IMFs. The closer the revised spectral coherence is to one, the higher the spectral dependence of two successive IMFs is. Additionally, a fusion rule based on locations of local minima of the revised spectral coherence is proposed to automatically fuse successive IMFs with similar characteristics into a new IMF, called an enhanced IMF (EIMF). Vibration signals including simulated and real multi-fault signals are used to verify the enhanced EEMD. A comparison with EEMD is conducted to show the superiority of the enhanced EEMD. The results demonstrate that the enhanced EEMD has better performance than EEMD for automatically extracting periodic components and random transient components from a single-channel vibration signal mixture.